Afshan Tabassum
Technology is slowly but surely advancing into every sector and is gradually expanding into the agricultural industry as well. The need for structured and effective crop farming makes the employment of technology imperative. By 2050, the UN predicts that there will be 9.6 billion people on earth, up from the current 7.2 billion and our country which is the second most populous nation will face a similar perilous situation. Making farms more productive and equipped to meet food demand is a huge undertaking that governments around the world must undertake. How would the government know about its farmlands and whether they could produce enough food for its citizens in such a situation? For the Indian authorities, the situation is made even more difficult by the fact that the majority of farms still practice traditional farming and are not tracked. A method known as the Crop Cutting Experiments (CCE), which provides authorities with more exact data on crop production, is saving their bacon. The government and agricultural organizations estimate the yield of a crops and a region during its cultivation cycle using this methodology. A conventional approach of CCE is yield component method, which selects specified places and random samples from the entire region for investigation. Following the selection of the plot, the harvest from that region is assessed based on many criteria, including grain weight, biomass weight, moisture, and other pertinent elements. A rough estimate is created from the yield of the state or region and is generalized for the entire region based on the data gathering. The Pradhan Mantri Fasal Bhima Yojana (PMFBY) makes use of the information gathered via the conventional CCE method. The central government’s plan makes it easier for insurance firms to process farmers’ requests for coverage. For each crop, each state is required by PMFBY to conduct four CCEs in each gram panchayat and submit a yield report within a month of harvest. The government gains from this CCE yield report because it allows them to strategically plan their agricultural policies and programmes. Similar to this, banking organizations also obtain the necessary inputs before providing insurance coverage in case of crop failure or a subpar yield. Additionally, it opens up the possibility of creating insurance plans that are specific to each location. The conventional CCE approach, however, is not without flaws, where dependence on factors including administrative setup, quantity and type of field crew, harvest conditions, and farmer collaboration continues to be the major problem. The situation is still dire in a nation like India, where there are about 2.5 lakh gram panchayats. Factors like insufficient and untrained labour and a short time window for completing studies add to the worries. Additionally, the need for a more effective method is becoming increasingly important given the brief harvest window. With many technical developments and satellite imagery, the conventional CCE method in agriculture is being made effective and error proof. More carefully chosen CCEs points are being used, and the yield is estimated in a timely manner.
Artificial Intelligence based agritech systems can give ground level data and satellite imaging for identifying plots that are suited for such trials making the CCE experiments more precise and scientific. With the development of cutting edge technologies, government officials can now locate the ideal plot for CCE and obtain results that are more scalable, accurate and scientific. These reports have numerous advantages for insurance firms in terms of processing claims and determining agricultural yields. With limited staff and more precise reports, the government can complete the most CCE tests during the limited harvest window. Insurance firms find this approach valuable since it provides them with more accurate reports. Additionally, they can provide farmers with customized crop insurance plans and carry out equitable and prompt claim payouts. Accurate report generation can result in improved yield and quicker claim processing, which greatly eases the strain on farmers. It’s a move that is expanding in scope across the nation and the world. In India, scientists have teamed up to develop a smart approach to promptly estimating the nation’s crop yields, replacing time consuming old sample techniques. The first of these smart initiatives were launched in the fall of 2019 at the Mahalanobis National Crop Forecast Centre of the Ministry of Agriculture and the Indian Space Research Organization (ISRO). These crop cutting experiments (CCE) represented a significant departure from the random sampling practices that had previously been used. Based on the ground breaking research of statisticians like P. C. Mahalanobis which have been conventional basis for estimating crop productivity in our country, where samplers gather crops from plots of a specific size and form with a view to calculate the ultimate yield per hectare were conducted as a part of India’s General Crop Estimation Survey, in which more than 1 million CCEs for various crops were planned annually in villages and plots chosen at random. Each major crop growing district carried out more than 100 tests, whereas more than 45 were minor ones. Since the begging of national crop insurance scheme the number of CCEs that had to be conducted has significantly increased, where a community or village panchayat needs at least four CCEs for a significant crop. As soon as this happened, the number of CCEs skyrocketed from a million to roughly 7-8 million annually. Due to the short harvest period, it was incredibly challenging to complete so many CCEs with the available staff. To overcome this challenge India’s agriculture ministry ordered pilot studies to create satellite and other cutting edge technologies for CCE optimization during kharif and rabi 2018-19, where it was demonstrated that it is possible to decrease the number of CCEs by more than 60% while retaining accuracy by employing satellite data and modelling techniques. By using this approach, it is also possible to minimize the number of CCEs while preserving accuracy.
Future crop estimation procedures by means of hi-tech approaches will have positive implications which will assist farmers in getting their legitimate claims at the appropriate time. The existing sampling procedure for crop yield estimation will undergo a paradigm shift as a result of technology based yield estimation will be beneficial in all areas of agricultural policy and planning.
(The authors work in SKUAST-Jammu
(Inputs by M Iqbal Jeelani, Imran Rashid)
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